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    ViewBag.Title = "About";
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    <p>
        Machine learning, a branch of artificial intelligence, is about the construction and study of systems that can learn from data. For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders.
The core of machine learning deals with representation and generalization. Representation of data instances and functions evaluated on these instances are part of all machine learning systems. Generalization is the property that the system will perform well on unseen data instances; the conditions under which this can be guaranteed are a key object of study in the subfield of computational learning theory.
There is a wide variety of machine learning tasks and successful applications. Optical character recognition, in which printed characters are recognized automatically based on previous examples, is a classic example of machine learning.
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    <p>
        <h3>Definition</h3>
        In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed".
        Tom M. Mitchell provided a widely quoted, more formal definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E". This definition is notable for its defining machine learning in fundamentally operational rather than cognitive terms, thus following Alan Turing's proposal in Turing's paper "Computing Machinery and Intelligence" that the question "Can machines think?" be replaced with the question "Can machines do what we (as thinking entities) can do?
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    <p>
        <h3>Generalization</h3>
        Generalization in this context is the ability of an algorithm to perform accurately on new, unseen examples after having trained on a learning data set. The core objective of a learner is to generalize from its experience. The training examples come from some generally unknown probability distribution and the learner has to extract from them something more general, something about that distribution, that allows it to produce useful predictions in new cases.
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    <p>
        <h3>Machine learning and data mining</h3>
        These two terms are commonly confused, as they often employ the same methods and overlap significantly. They can be roughly defined as follows:
        Machine learning focuses on prediction, based on known properties learned from the training data.
        Data mining (which is the analysis step of Knowledge Discovery in Databases) focuses on the discovery of (previously) unknown properties on the data.
        The two areas overlap in many ways: data mining uses many machine learning methods, but often with a slightly different goal in mind. On the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in Knowledge Discovery and Data Mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.
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    <h3>More on machine learning</h3>
    <p>
        A bunch of links with interesting stuff on machine learning.
    </p>
    <ul>
        <li><a href="http://en.wikipedia.org/wiki/Machine_learning" target="_blank">Wikipedia</a></li>
        <li><a href="http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1" target="_blank">Stanford courses</a></li>
        <li><a href="http://hunch.net/" target="_blank">Machine Learning (Theory)</a></li>
        <li><a href="http://www.ml.cmu.edu/" target="_blank">Machine Learning Department (CMU)</a></li>
    </ul>
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